1. Field of Endeavor
The invention pertains to the sensing of, and sensors for the detection of, infrasound (pressure variations with frequency content under 10 Hertz). The present invention concerns a method for removing the effects of undesirable disturbances from the measurement of infrasound. The discussion that follows focuses on air, but the invention is not limited to air as the medium for transmission of infrasound.
2. Infrasound Prior Art
Infrasound is typically classified as low frequency (under 10 Hertz) pressure waves with long wavelengths (greater than 30 meters in air). These signals are outside the hearing range of humans. These signals have been detected with a variety of devices, including highly sensitive barometers, differential pressure sensors, and fiber optic interferometers. The typical purpose for measuring infrasound is to detect the source of generation of the infrasound. Sources of infrasound range from man-made, such as bridge vibration, nuclear explosions (Comprehensive Test Ban Treaty) and industrial processes, to naturally occurring events, such as: volcanoes, meteorite impact, tidal action, tornadoes, tsunamis, avalanches, earthquakes, ground swell prior to earthquakes, . . . etc. The pressure variations that cause infrasound are small relative to the ambient pressure, thus the detectors must be very sensitive. This sensitivity allows the detector to pick-up pressure changes that are not caused by the source of interest, in particular wind. Much of the current research in infrasound sensing is on techniques for eliminating the wind disturbance from the pressure measurement.
The primary technique for reducing the winds contribution to infrasound signal is a spatial filter. A spatial filter involves a distributed measurement. Such a measurement is made over a length or area to average-out local pressure variations due to the localized affects of wind. For pneumatic differential pressure sensors a spatial filter typically involves a network of perforated pipes or permeable hoses that feed a single volume that is used as the source of the differential pressure measurement. Bedard et al. (2004) presented the results from the deployment of such a system. The sensor included a spatial filter for reducing wind-induced pressure fluctuations, which was composed of twelve porous hoses arranged in a diameter of about 50 feet, FIG. 1. Zumberge et al. (2003) presents the results of an optical fiber infrasound sensor (OFIS) that measures, interferometrically, pressure-induced strain in a sealed tube. The OFIS system has a length of 89 meters (left, FIG. 2) and is compared to a pneumatic pipe array on the right in FIG. 2. The OFIS measurement is distributed over a length. The “line receiver”, implementation of OFIS has directional sensitivity and has a frequency roll-off for signals sources not at 90 degrees to the line (Zumberge et al., 2003). This directional sensitivity is by design. The OFIS is described in U.S. Pat. No. 6,788,417 B1 (May 2004). U.S. Pat. No. 6,788,417 also summarized the prior art on wind noise in infrasound measurements and includes a discussion of the prior air of noise reduction methods using perforated pipes and permeable hoses. In Noble and Tenney (2003), the U.S. Army documents the use of a sensor array with 20-meter spacing to detect impulsive signals in the frequency range of 3–8 Hz. More recently, Stubbs et al, (2005), “examines ways to enhance the effectiveness of infrasound monitoring”, including arranging the filter hoses to increase directional sensitivity. All of the above techniques discuss distributed measurements that achieve wind-borne noise reduction using some type of spatial filtering.
A disadvantage of the above approaches to reducing the contribution of the wind to the measurement of infrasound is that the implementation of spatial filtering is relatively large physically. The array in Noble and Tenney (2003) is 20 meters in diameter and the OFIS in FIG. 2 is 89 meters long. The pneumatic array FIG. 2 is on the order of 50 meters in diameter. The hardware used for the spatial filters is continuous, not discrete components. The measurement is an average over the length of pipe or hose. These implementations of spatial filtering require real estate proportional to the wavelength of interest, necessitating the location of the sensor in areas where land is available. The land must also be maintained and secured, which requires a recurring labor cost.
Another disadvantage of the above approaches is that while in use, these sensors must be stationary. Vertical displacements as a function of time could be perceived as infrasound since atmospheric pressure is correlated with altitude. The distributed sensors with spatial filters assume that the ground, averaged over the sensor length, is fixed and not moving and thus not a contributing factor in the measurement of infrasound. This confines the sensor to a stationary position.
Another disadvantage of the above approaches is that because they are large, and in some cases buried underground, they are not easily relocated. Deployment of these sensors is typically permanent. It may be possible to transport the sensor to another location, but the sensor is not easily moved and reused at another location. This precludes the reuse of these sensors at alternative locations.
Another disadvantage of the above approaches is that the filtering technique is a function of hardware. These implementations of spatial filtering are a result of the distributed and continuous nature of the sensor hardware. Thus, the filtering technique is fixed and cannot be changed. In Bedard et al. (2004) the filter is a function of the geometry of the permeable hoses. In Zumberge et al. (2003) the filter is a function of the orientation and length of the OFIS. Spatial filtering is essentially an averaging filter, averaged over the length of the OFIS or pipe array. As a filtering technique, averaging is only optimal if the time domain properties are to be maintained, as in a sudden or step change as described in Smith, (1999). If it is desired to maintain frequency domain characteristics of the signal, then filtering techniques other than averaging will lead to improved results. If the signal source of interest is periodic (a tornado for example) then frequency separation of the measured signal is desired. An averaging filter cannot separate one band of frequencies from another because the averaging filter has poor stop-band attenuation properties. For periodic signals an averaging filter is not the right filter choice to get the best frequency separation and the above approaches are locked into the use of an averaging filter.
3. Adaptive Filtering Prior Art
Adaptive filters have been incorporated into numerous patents, commercial devices and software. The commercial computer program MATLAB® Signal Processing Toolbox from Mathworks, Inc. (Natick, Mass. USA) contains an implementation of a “Kalman Adaptive Filter”. The Masimo SET™ oximeter sensor uses an adaptive filter to remove undesired pulse components from the measure of blood oxygen, (U.S. Pat. No. 6,036,642 and RE38,492E). In Purdon et al (2001), an adaptive filter is used to remove an unwanted signal from an EEG measurement using a head motion sensor. U.S. Pat. No. 6,675,036B2, by Kreger et al (2004) describe a medical imaging devices that uses an adaptive filter to reduce the affects of bio-potential signals caused by a patient's respiration. In U.S. Pat. No. 5,943,641, Carme, et al (1999) describes a device for “recovering a wanted acoustic signal from a composite acoustic signal including interference components”. Carme et al claims acoustic signals for both the composite and the reference acoustic signals. All of the above adaptive filters use similar techniques. These adaptive filters all use at least one additional measurement. This additional measure is correlated with some undesired component. This additional measure is filtered, then used to reduce the contributions of the undesired component in the primary measurement of interest. In the above patents, it is the combination of signals used in the measurement that is unique, not the adaptive filter algorithm of which there are many. All of the adaptive filter algorithms work similarly to minimize a cost function, typically a mean squared error, such as the Least Mean Squared (LMS) algorithm.